Research Article Real-Time Communications in Large-Scale Wireless Networks

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1 Hindawi Publihing Corporation International Journal of Digital Multimedia Broadcating Volume 2008, Article ID , 16 page doi: /2008/ eearch Article eal-time Communication in Large-Scale Wirele Network Liang Song and Dimitrio Hatzinako Edward S. oger Sr. Department of Electrical and Computer Engineering, Univerity of Toronto, 10 King College oad, Toronto, ON, Canada M5S 3G4 Correpondence hould be addreed to Liang Song, eceived 25 March 2008; evied 13 July 2008; Accepted 17 September 2008 ecommended by Maimiliano Laddomada There i an emerging need for realizing real-time quality of ervice (QoS) over multihop wirele communication in large-cale wirele network. The application can include wirele meh infratructure for broadband Internet acce upporting multimedia ervice, viual enor network for urveillance, and diater-relief network. However, a number of challenge till exit a revealed by recent work, where the dataflow QoS performance uch a throughput and end-to-end delay can degrade fat with the number of wirele hop. We propoe to ue large-cale cognitive networking method to reolve the wirele multihop challenge. By the cognitive-networking concept, data packet travel along opportunitically available path in the network with opportunitically available pectrum in every hop. eliable end-to-end communication can be achieved for real-time ervice, where we how that (1) dataflow throughput can be independent of any number of wirele hop, (2) end-to-end delay and delay variance increae linearly with the number of wirele hop, and (3) delay variance decreae to zero with higher network denity. Thee reult are upported by analyi, imulation, and experiment. Copyright 2008 L. Song and D. Hatzinako. Thi i an open acce article ditributed under the Creative Common Attribution Licene, which permit unretricted ue, ditribution, and reproduction in any medium, provided the original work i properly cited. 1. INTODUCTION Large-cale wirele network, for example, mobile ad hoc network, wirele meh network, a well a wirele enor network, have been receiving ignificant attention in the pat few year. ecently, there ha been growing interet in realizing real-time quality of ervice (QoS) over multihop wirele tranmiion in large-cale wirele network. For example, the driving commercial application include wirele meh infratructure for broadband Internet acce [1], upporting Voice over Internet Protocol (VoIP) or Multimedia over IP, and wirele enor network with real-time event detection/reporting, epecially in video urveillance, that i, viual enor network [2]. Compared to the claical many-to-one (tarlike) network, uch a cellular phone network and WiFi- (IEEE ) [3] enabled wirele local area network (WLAN), a wirele meh-baed infratructure for broadband acce ha the advantage of much lower capital and operational cot and can achieve higher ervice coverage. A one example, municipal WiFi meh network are rolling out in a number of major citie, providing wirele web urfing and ervice to general public. Conidering the popularity of Internet voice and video, further development for upporting VoIP within the wirele meh infratructure are highly demanded. On the other hand, viual enor network appear to be one of the killer application for wirele enor network, ince a lot of major citie have intalled publicadminitrated urveillance camera for variou purpoe. A of now, thoe camera are normally attached by cable. By adopting wirele connection, there will be ignificant cot reduction in the intallation, which can enable much dener deployment. Moreover, related application alo include the diater-relief wirele network, with ad hoc formation, where the real-time information delivery can be of life and death importance. However, many challenge till exit in realizing real-time QoS over multihop wirele network. It ha been well known that dataflow throughput, end-to-end delay, and delay jitter can degrade fat when the number of wirele hop increae [4, 5]. The limitation can be primarily introduced by dynamic network-reource availabilitie including both pectrum bandwidth and wirele node/radio. Specifically, random pectrum availability i baed on wirele fading

2 2 International Journal of Digital Multimedia Broadcating and interference prevailing in unlicened band (e.g., ISM band); and random radio availability i due to dynamic traffic load (congetion) and other factor uch a radio failure. In traditional wirele networking, the media acce control (MAC) layer et up logic wired link over wirele media, which aume predetermined pectrum availability. On top of it, the network layer run ad hoc routing protocol which aume predetermined radio availability and network topology. It i difficult to have effective QoS model in the traditional network protocol tack [6], when both pectrum bandwidth and radio availabilitie cannot be predetermined in large-cale wirele network. In order to reolve the wirele multihop limitation, there have been propoal to intall multiple radio in one ingle wirele node [4] and deign network routing protocol baed on wirele link tatu, a well a other combined metric [5]. Although evidence how that improvement could be obtained, thee approache do not addre realtime QoS. Moreover, exiting approache can generally uffer from the lack of calability. Typically, the complexity of exiting protocol can grow fat with the number of wirele node neighboring each other (higher network denity), ince there are a large number of link to be managed and/or tracked on every node. Other reearch work that invetigate real-time QoS in wirele network could be baed on inappropriate aumption for large-cale wirele network, uch a the aumption on fixed link bandwidth [7]. In thi paper, we apply the method of large-cale cognitive networking [8] to reolve the wirele multihop limitation. The concept of cognitive networking opportunitically utilize network reource including both pectrum bandwidth and wirele node/radio availability to realize reliable communication in large-cale wirele network. The adopted network architecture i the Embedded Wirele Interconnect (EWI) [8 11], where wirele linkage i redefined introducing abtract wirele link. By definition, abtract wirele link can be arbitrary functional abtraction of mutual cooperation among proximity wirele node, where the categorie can include broadcat, unicat, multicat/anycat, and data aggregation, among other. Therefore, wirele link module are deigned a the unit for realizing different abtract wirele link at individual wirele node. The architecture of EWI i then developed upon two layer, where the bottom wirele link layer upplie a library of wirele link module; and the upper ytem layer can organize the module for achieving effective application programming interface (API). Conforming to the cognitive networking concept, both the operating pectrum and the participating node of an abtract wirele link i opportunitically decided baed on their intantaneou availabilitie. By the unicat module deign in the cognitive networking, data packet can take opportunitically available path in the network from ource to detination, with opportunitically available pectrum in every hop. In comparion, dynamic change in pectrum and radio availability can create bottleneck on the routing path of traditional wirele networking, where uch bottleneck are all reolved by the propoed method. Therefore, reliable end-to-end communication can be achieved, by the opportunitic utilization of network reource. The preented unicat module deign i alo implemented by cognitiveradio prototyping [12]. In the performance analyi, realtime QoS metric including throughput, expected end-toend delay, and delay variance, are tudied. The following propoition are obtained. (i) All the invetigated real-time QoS metric improve with larger network cale, and the complexity on individual node remain contant independent of any network cale. It i alo hown that the performance dynamic, a indicated by end-to-end delay variance, can diminih to zero with higher network denity. (ii) eliable communication can be achieved over a large number of wirele hop. In principle, the throughput can be independent of the ource-todetination ditance; while the expected end-to-end delay, and the delay variance, increae linearly with the ource-to-detination ditance. (iii) Conidering the reource contraint, uch a energy conumption or network capacity, we identify that radio tranmitting power can be an effective control knob for traffic prioritization in dealing with the tradeoff between achievable QoS performance and reource conumption. Therefore, the two major contribution of the paper are the following: (1) the firt deign & implementation of real-time communication for large-cale wirele network; (2) the QoS performance analyi of the deigned realtime unicat module. In what follow, related work are urveyed in Section 2; the unicat wirele link module deign and implementation are decribed in Section 3; the performance analyi of real-time QoS metric i preented in Section 4; the imulation and experiment reult are reported in Section 5 and 6, repectively; the concluion i given in Section ELATED WOK elated work in cognitive networking and the EWI network architecture have previouly appeared in [1, 8 12]. A a pilot architectural reference model, EWI wa firt introduced in the application-pecific tudie in wirele enor network [10, 11]. In [9], recent reearch work in the cro-layer deign of wirele enor network are urveyed, where it i uggeted that EWI can be an unified deign architecture. The concept of large-cale cognitive wirele network ha been preented in [8], where EWI wa adopted a the network architecture. In [1, 12], the EWI i further utilized to contruct a ubiquitou wirele meh infratructure for broadband Internet acce. The unicat deign can alo be related to exiting reearch work in opportunitic routing [13 17]. In principle, opportunitic routing cheme deal with the codeign of routing and MAC protocol. However, previou reearch work uually deal with ingle-hop performance metric, while

3 L. Song and D. Hatzinako 3 the contribution of thi paper deal with end-to-end QoS for real-time communication. Moreover, contribution are made in large-cale cognitive networking, with a report of experiment reult. In [13], the protocol elect the next hop relay node by a lotted ACK (acknowledge) mechanim. Having uccefully received a data packet, the node calculate a priority level, which i inverely proportional to the ditance between the node and the packet detination. The node with the highet priority i then elected ditributively a the next hop relay node. In [14, 15], a technique named GeaF wa propoed, where the node forwarding region i divided into maller ection with different prioritie, a decided by the forwarding ditance. The performance analyi of GeaF included the ingle-hop energy conumption and latency tradeoff, a well a the multihop count number. The tudy in [16] invetigated the architectural apect of opportunitic routing, where the performance of the component deign i analyzed baed on ingle-hop metric and light-traffic cenario. In [17], the author dicued the opportunitic relay node election in a two-hop tranmiion cenario. The relay-node priority criterion i baed on the gain of ourceto-relay channel and relay-to-detination channel. 3. MODULE DESIGN AND IMPLEMENTATION A unicat wirele link i an abtraction of the proximity wirele node cooperation for unicating. Since unicating uually involve the information delivery from ource to detination over multiple hop, the unicat module deign decouple the end-to-end network behavior into proximity wirele node cooperation. In the following, ome element of the module deign and implementation are preented. The module tate-diagram i then hown Network addre Network addre i related to the context, ubject to a cot of delivery criteria. Let d and denote the network addree of the detination node and the ource node, repectively. Given the detination d of a data packet at one wirele node n, a local parameter c n,d i aumed obtainable, which indicate the approximate or average cot of delivering the packet to the detination from the node n independent of dynamic change. In large-cale wirele network, the cot of delivery c n,d i uually a function of the ditance from one local node n to the detination d. In location-centric network (e.g., [10]), where wirele node are aware of their own location, for example, by global poition ytem (GPS) or triangulation etimation, the network addre of one node n i olely decided by the node n location coordinate L n. Given the detination coordinate L d of a packet, c n,d = L n L d can be readily calculated in geometry, defined a the pecific ditance. In data-centric network (e.g., [18]), the network addre can be decided by the application pecific data. The cot of delivery c n,d i the application data gradient, which can be aumed a a monotonically increaing function of the ditance between n and d. In a data-collecting or fuion network, for example, wirele enor network, the ink (data collector) can broadcat a number of identity advertiement packet, which i thereon flooded in the network, by broadcating. Every node can count the average mallet number of hop from the ink, on receiving the advertiement packet. The count number can be ued a c n,d for one node n. A imilar approach appeared in [19] yet in a more general cenario, where the logic addre, that i, a vector of the etimated ditance or hop number to a group of anchor, i maintained for every wirele node. New node joining in the network can etimate their own logic addre by acquiring the addre of neighboring node. Although other type of addre can be applicable, we ue the location addre in thi paper for analyi, imulation, and experiment adio implementation The terminology of cognitive radio wa firt uggeted in [20] a an ideal-omnipotent radio which can take all the parameter into conideration for uer-centric communication. Cognitive radio wa later reviewed in [21, 22] a the radio with dynamic pectrum acce. In [8], two propoition are further uggeted for the cognitive radio for large-cale wirele network. (i) The radio can opportunitically ene the pectrum reource, o that the elected pectrum uage will not be interfering with other on-going wirele communication. (ii) The radio can opportunitically poll one or more other proximity radio onto the elected pectrum, o a to realize certain type of local cooperation. The above two propoition can extend the concept of pure cognitive radio to cognitive network, which implement both dynamic pectrum acce and dynamic radio acce. In the network architecture EWI, a wirele node can initiate an abtract wirele link, that i, certain type of local cooperation among a number of proximity wirele node. Hence, both the et of wirele node and the operating pectrum are opportunitically decided for an abtract wirele link. Further, the initiated link will not be interfering with other wirele communication, in accordance with the firt cognitive propoition. In the tet-bed development of the paper, we prototyped an experimental tone-baed cognitive radio [8, 12]. Specifically, the radio can acce a group of predetermined data channel, where every data channel i alo aociated with two ditinctive frequency tone, that i, one ening tone and one polling tone. The ening and polling tone are at ditinctive frequency different from the data channel. The radio hardware i therefore compoed of two tranceiver, which are the tone tranceiver and the data tranceiver, repectively. On initiating an abtract wirele link, the radio ene for an available channel, with the vacant data channel and the vacant ening/polling tone. It then broadcat the polling tone aociated to the elected channel, to poll it

4 4 International Journal of Digital Multimedia Broadcating urrounding node. On detecting the riing edge of the polling tone, the urrounding node can decide to join in the initiated abtract wirele link baed on their autonomou availability. On joining in an abtract wirele link, the radio of the urrounding node alo broadcat the ening tone. A uch, both ening and polling tone protect wirele link module from pectrum interference. We note that the ue of frequency (buy) tone ha alo appeared in numerou work of MAC protocol [23, 24]. However, here we have the firt implementation over multiple channel, and uch implementation i in the context of large-cale cognitive wirele network. The unicat wirele link module i programmed on the cognitive radio prototype (hown in Figure 1). The experiment hardware i a tack of two radio board (TELOSB [25]). The radio board i compoed of one IEEE [26] compatible tranceiver, Chipcon CC2420, one TI MSP430 Microcontroller, and the program interface. The top radio board i utilized a the tone tranceiver, while the bottom radio board i utilized a the data tranceiver. The two are wired up by the digital interface hown in Figure 1. Three independent data channel in the 2.4 GHz band are allotted to the radio platform, while the aociated tone are alo in the band of 2.4 GHz. The unicat module i programmed in the firmware of TI MSP Packet relaying proce Under an initiated wirele link, the packet relaying proce pick one available relay node and end the data information from the ource or a previou relay node to the new relay node. Four type of packet are ued in the packet relaying proce, which are TS, CTS, DATA, and ACK, repectively. TS, CTS, and ACK are control packet, while DATA i the data information unit. Let node n p denote the relay node at the hop p, the next hop relay node n p+1 i found by the following procedure, which incur local wirele node cooperation in proximity, that i, a unicat wirele link. Firt, the node n p broadcat a TS, including the module type (unicat), the detination addre d, andthe elf-addre n p. Upon receiving the TS at one node n, if the condition c n,d <c np,d i atified, the node n initialize a timer, with the timeout period a T d (n p, n, d). T d (n p, n, d) iinverely proportional to c np,d c n,d, which i determined by the pecific phyical radio technology, for example, ( T d np, n, d ) C 0 = + SIFS, (1) c np,d c n,d where C 0 i a contant, and n d. SIFS i the mallet (minimal) inter frame pace (delay contant), which i compoed of the module proceing time and the tranceiver X/TX witch time. In our current tet-bed implementation, T d (n p, n, d) i alo lotted according to the minimal carrierening time. Note that the condition c n,d <c np,d i enforced, o that the cot of delivery to the detination i alway decending which prevent any loop. The node n then back off and monitor the data channel for the period T d (n p, n, d). If the data channel i free during Figure 1: Cognitive radio prototype. that period, the node n replie with a CTS, declaring itelf a the next hop relay node n p+1. Otherwie, the node n quit the unicat module. A uch, the node with the mallet cot c n,d hould be elected a the next hop relay node n p+1. After having received the CTS, the node n p tranmit the DATA packet, and after receiving the DATA packet, the node n p+1 replie an ACK, completing the round of relaying. In the cae that more than one node end out their CTS imultaneouly, the tranmiion will collide on ome pecific bit, for example, the node addre, where the CTS packet are different. Since thi colliion can be detected by certain mechanim, uch a cyclic redundancy check (CC), at n p, the node n p can ue an appended procedure to differentiate one node a n p+1, either before or after the DATA tranmiion. The decribed mechanim i illutrated in Figure 2. In the lat hop, the detination d i alway aigned with the minimal delay, that i, T d (n p, d, d) = SIFS Module tate diagram The tate diagram of a ingle unicat module in the wirele link layer i hown in Figure 3, which can give a ummary of the implementation. The definition of the tate and the aociated tranferring branche can be elf-explainable, in accordance with previou decription. The wirele link layer tay in the IDLE tate, when no module i involved. It can initiate a unicat module, for example, on receiving the command from the ytem layer, o a to end certain amount of information (i.e., contained in one DATA packet) to a pecified detination. In the IDLE tate, the wirele link layer alo monitor the et of polling tone, which are aociated to the predetermined group of data channel. On detecting the riing edge of one polling tone, it end one module requet to the ytem layer. Upon approval, the wirele link layer liten to the TS packet on the data channel, which i aociated with the detected polling tone. Otherwie, it goe back to the IDLE tate. Furthermore, after having received one DATA packet, the unicat module automatically relay it, if the current node i not the detination. Table 1 how an example et of primitive function on the interface between the ytem layer and the wirele link layer, a related to the unicat module. The parameter Priority in Table 1 can be ued for pecifying the traffic cla, which will be explored further in Section

5 L. Song and D. Hatzinako 5 Node n p Sending TS Liten eceiving CTS SIFS Sending DATA Liten eceiving ACK Elected Sending node eceiving TS T d n p+1 CTS Liten eceiving DATA SIFS Sending ACK Carrier ening Other node eceiving TS T d Sleep Carrier ening Figure 2: Packet relaying mechanim Dicuion By opportunitically dealing with network reource including both pectrum bandwidth and radio availability, data packet take opportunitically available path with opportunitically available pectrum in every hop. Since the unicat module operate opportunitically in proximity wirele node, the end-to-end performance of real-time QoS, can be tatitically aured, adding up the equential proximity operation. Thi dicuion can provide an intuitive explanation of the performance analyi reult in Section 4. Specifically, random pectrum availability i handled by the cognitive radio implementation, where a ource or relay node trie to find an available data channel for initiating the unicat wirele link. Important apect of random node/radio availability are a follow: the node deployment, mobility, and traffic congetion. In general, it indicate that the node n p, that i, the pth hop relay, would be uncertain about the availability of the next hop relay node, before the TS probing. The TS/CTS exchange in the module deign opportunitically find the next hop relay, in a group of available candidate. In traditional computer network, dynamic traffic load i limited by network congetion control. The network layer drop overflowed packet by queueing management, which i then detected by the tranport layer. Such method have been known to incur problem in wirele network [27]. By the real-time unicat module, the paradigm of the claical network queueing could be tranferred to queueing in network. In an individual relay node, a DATA packet i forwarded automatically without lengthy buffering in one local queue. Sequential DATA packet nominally take opportunitically decided path from the ource to the detination, which are buffered in the network, intead of any predetermined node. Therefore, it can be intuitive to conceive that the QoS performance metric hould improve with larger network cale, ince the network denity contribute to the diverity (or more radio reource) that can be opportunitically exploited. Thi agree with other theoretical reult, for example, in [28], where it i tated that ummed network tranport capacity hould increae with network cale, on the order of Θ( N), where N i the number of node. Moreover, in the propoed method, any node doe not need to conider or know every other poible node a a candidate for the next hop. In the formation of abtract wirele linkage, the participating node are decided by their autonomou availability. The unicat module deign alo aure that there would only be one TS repone in mot cae by the carrier-ening mechanim. Therefore, the module complexity at individual node remain contant, independent of the network cale. 4. PEFOMANCE ANALYSIS OF EAL-TIME QoS The performance analyi mathematically quantifie the major propoition in the paper. The reult can alo provide ueful network planning guidance for deployment Objective and approache In upporting real-time QoS, the metric of throughput, expected end-to-end delay, and delay variance, are tudied for the unicat wirele link module. Two type of reource conumption are conidered, which are network capacity and network energy conumption. Therefore, we centrally explore the following three quetion. (1) How do the QoS metric change with network cale? (2) How do the QoS metric change with the ource-to-detination ditance? (3) How to control the reource allocation, o a to achieve the QoS requirement? Given the ource and the detination d ofaunicat dataflow, let l = L L d denote the ource-to-detination ditance. Analytical reult are obtained under the aymptotic condition of long-range unicating, l, where the jutification can be that long-range unicating preent the wort-cae cenario of the network performance under practical interet. On the other hand, concie analytical formulation of the QoS metric can be obtained under thi long-range aymptotic condition, which can provide analytical inight to previou quetion Analytical model In the performance analyi, model about node ditribution, wirele channel, power conumption, bandwidth availability, and the IDLE probability are adopted.

6 6 International Journal of Digital Multimedia Broadcating Table 1: Exemplary primitive function related to the unicat module. Function Decription Direction SendUnicat(DA, DATA, Priority) Command of ending the unicat DATA to the detination addre DA; From ytem to wirele link StatuUnicat(&Statu) eturn the command tatu of endunicat ( ), for example, Succe From wirele link to ytem or Fail; IndicateUnicat(SA, DATA, Priority) Indicate the received unicat DATA from theourceaddresa; From wirele link to ytem Moduleequet() equet for litening to the TS, that i, defined in the unicat module; From wirele link to ytem Moduleepone(&Statu) eturn the requet tatu of moduleequet(), that i, Permitted or From ytem to wirele link Denied; GetState(&State) Getthecurrenttate. From ytem to wirele link Node ditribution model The node ditribution i modeled a a two-dimenional (2D) Poion proce [29], with the node denity λ. That i, given an area A of the ize A in the field, the number of node in the area follow Poion ditribution with the parameter λ A. The Poion modeling can be typical for random node ditribution and/or random mobility Wirele channel model Given an arbitrary tranmitting node n, and a receiving node m, the ucceful tranmiion probability, in general, i a function of the tranmitting power P t, the radio data rate, and the ditance ζ = L m L n.wedenotethifunctiona f (P t,, ζ). For example, if mall-cale ayleigh fading [30] i aumed, the channel model can be given by ( ) Pt G ξ f (P t,, ζ) = Prob N 0 ζ α B = e (N 0 ζ α B)/(P t G). (2) In (2), G i a propagation-lo contant; N 0 i the receivernoie power pectrum denity; α i the path lo component in wirele channel [30]; B i a threhold contant repreenting the receiver enitivity; and ξ i a unit-mean exponential random variable. Thi model will alo be utilized to obtain the numerical reult in Section Power conumption model eferring to the tate diagram in Figure 3, the node power conumption at the IDLE tate i denoted by P I, that i, the low-power monitoring of the polling tone; the power conumption at the tate of Send DATA, Send TS, Send CTS, and Send ACK, i modeled by P S +(1+β) P t, (3) where P S i the tranmitter circuit power conumption; β i a contant decided by the F power amplifier efficiency [31]; and P t i the tranmitting power a defined perviouly. The node power conumption in other tate or time i modeled by a contant P, which denote the receiving (or idle litening) power conumption Bandwidth availability and the IDLE probability When initiating a unicat module, we aume that the node can alway find an available channel by the cognitive radio without ignificant delay. The IDLE probability i the probability that a given node i in the IDLE tate (hown in Figure 3), and can be engaged in a unicat wirele link being initiated by other node around it. We aume that there i a fixed (or lower bounded) IDLE probability ρ, in the analyi. With limited network capacity and arbitrary traffic load, thee two aumption need to be enured by an appropriate call admiion control (CAC) mechanim. On the other hand, given analogou experience of the popular video treaming on peer-to-peer overlay network, it could alo be appropriate to aume no CAC mechanim, while the traffic volume demanding can alway fall below the planned (or unplanned) network capacity. In large-cale wirele network, the capacity could be of relatively cheap reource, given abundant wirele node and the ue of unlicened band, while the traffic load i not necearily related to the number of wirele node. Thee conideration give the optimization formulation under different type of reource conumption in Section Other parameter and notation The packet length (bit) of TS, CTS, DATA, and ACK are denoted by L, L C, L D,andL A,repectively.T S denote the time delay in the channel ening tate in Figure 3, and T B denote the time delay in the Backoff T d tate. We alo ue the notation E( ) andσ 2 ( ) to denote the mean and the variance of a random variable.

7 L. Song and D. Hatzinako 7 Module requet Permitted Liten for TS TS received Denied Detect polling tone No TS Channel buy IDLE ACK received Send DATA Backoff Channel free Send T d CTS Start unicat module CTS received Detination reached Data received Channel ening No CTS Send TS Find available channel Send ACK Not detination λ ρ f (P t,, ζ) on the forwarding plane, where ζ indicate the ditance to the node n i. Therefore, the uncompleted probability i decided by the equal-zero probability of a Poion random variable with the mean λ ρ Ω, which i given by e λρω. Ω i alway poitive, and i given by the integration of the channel model f (,, ) on the forwarding plane: Ω = 0 f ( ) P t,, x 2 + y 2 dx dy. (4) According to the above analyi, {τ i 1 i < I} can be modeled a a et of random variable with identical ditribution, where the expectation E(τ i )i Figure 3: Wirele link layer tate diagram. E(τ i ) = L + T S + T B + L C + L D + L A (1 e λρω). (5) 4.3. Iterative performance analyi Iterative performance can be imilar to ingle-hop performance. However, with the notation of iterative performance, we alo conider the cenario where the next hop relay node i not found in the tranferring branch from Send TS to channel ening of Figure 3. The reaon reide in the random node availability, for example, no potential relay candidate i in the IDLE tate. Due to thi difference, the terminology iteration intead of hop i ued in the following analyi, and iterative delay indicate the time delay of an iteration, wherea iterative energy conumption indicate the network energy conumption in one iteration. Particularly, conider that there are totally I iteration from the ource to the detination d. Let{n i 1 i I} denote the revelent et of node, which initiate the unicat wirele link. Obviouly, there i n 1 =, andn I end the DATA packet directly to the detination d. The iteration number I i therefore a random variable Iterative delay Let τ i denote the iterative delay at an iteration i. (Other poible model are alo dicued in Appendix C, which contribute to the ame end-to-end performance analyi reult.) If the DATA tranmiion i completed in the iteration, τ i can be calculated by the formula (L + L C + L D + L A )/ + T S + T B. Otherwie, if the DATA tranmiion i uncompleted ince no relay i found, τ i can be given by L / + T S + T B. Obviouly, for the lat iteration (i = I), the DATA tranmiion i completed. When i < I, the probability of uncompleted DATA tranmiion depend on the relay candidate availability on the forwarding plane. Due to the Poion node ditribution model with the denity λ, and the node IDLE probability ρ, the ditribution of relay candidate conform to an inhomogeneou Poion proce with the denity function Iterative energy conumption Let ɛ i denote the iterative energy conumption at an iteration i. (Other poible model are alo dicued in Appendix C, which contribute to the ame end-to-end performance analyi reult.) If the DATA tranmiion i not completed in the iteration, the energy conumption of the node n i i (T S +T B ) P +L / [P S +(1+β)P t ], according to the decribed model. The energy conumption of one relay candidate i given by T S P I +L / P. The number of the relay candidate around n i, which receive the TS, i a Poion random variable, with the mean value λ ρ Ω. HereΩ can be till given by (4), but i integrated on the backward plane, that i, 0 f (P t,, x 2 + y 2 )dx dy, intead of the forwarding plane. Therefore, the expected iterative energy conumption under uncompleted DATA tranmiion i the ummation: (T S + T B ) P + L / [P S +(1+β)P t ]+λ ρ Ω (T S P I + L / P ). If the DATA tranmiion i completed in the iteration, the energy conumption of the node n i i (T S + T B ) P + (L + L D )/ [P S +(1+β)P t ]+(L C + L A )/ P. The energy conumption of one relay candidate, which i not elected a the next node n i+1,itillt S P I +L / P. The number of the relay candidate i a Poion random variable with the mean value 2λ ρ Ω, including both the forward and the backward plane. On the node n i+1, the additional energy conumption i L D / P +(L C + L A )/ [P S +(1+β)P t ]. Therefore, the expected iterative network energy conumption under completed DATA tranmiion i (T S + T B +(L C + L D + L A )/) P +(L + L C + L D + L A )/ [P S +(1+β)P t ]+ 2λ ρ Ω (T S P I + L / P ). Since the DATA tranmiion i alway completed in the lat iteration, ɛ I i directly obtained from the previou dicuion. When i < I, imilar to the previou analyi of τ i, the probability of uncompleted DATA tranmiion i decided by e λρω. Therefore, {ɛ i 1 i<i} can be modeled

8 8 International Journal of Digital Multimedia Broadcating a a et of random variable with identical ditribution, where the expectation E(ɛ i )i E(ɛ i ) = ( T S + T B ) P + L [P ] S +(1+β)P t ( + λ ρ Ω T S P I + L P ) + ( 1 e λρω) { LC + L D + L A ] [P + P S +(1+β)P t Number of iteration ( + λ ρ Ω T S P I + L ) } P. Given all the parameter, the total iteration number I i a random variable decided by the ource-to-detination ditance l. It i hown in Appendix A that the mean value E(I) and the variance σ 2 (I)are E(I) = (6) l E(Λ) + o( l ), (7) σ 2 (I) = l σ 2 (Λ) E(Λ) 3 + o(l), (8) repectively. In the above, the random variable Λ generally indicate the forwarding ditance of one ingle iteration. Λ i defined with the pdf (probability denity function) p Λ (x): λρ e λρ x g(u) du g(x), x>0, p Λ (x) = e λρω δ(0), x = 0, (9) 0, x<0, where g(u) = f ( P t,, u 2 + v 2) dv. (10) Proved in Appendix B, we point out that the expectation E(Λ) i an increaing function of the node denity λ, but i upper bounded, a decided by the channel model f (,, ). Furthermore, the variance σ 2 (Λ) decreaetozero with higher λ. Given (7) and (8), both E(I) and σ 2 (I) can decreae with higher node denity λ. Thee relation can alo be intuitively undertood, which will be exploited for interpreting the end-to-end performance analyi reult next End-to-end performance analyi Analytical reult are preented for the maximal throughput Φ, the expected end-to-end delay Υ, the end-to-end delay variance Θ, and the expected network energy conumption per DATA packet Ξ, of a dataflow from the ource to the detination d, under the long-range aymptotic condition l. The dicuion how how thee metric change with the ource-to-detination ditance l, a well a the network denity λ Throughput The maximal throughput Φ i decided by how many DATA packet can be ent out from the ource to the detination d in a period of time, ubject to the condition that thi time period i much larger than the packet traveling time. According to previou analyi in Section 4.3.1, the probability of the completed DATA tranmiion in an iteration i 1 e λρω. Therefore, the expected number of iteration initiated by for one DATA packet i 1/(1 e λρω ), and the aociated time expectation i (L /+T S +T B )/(1 e λρω )+(L C +L D +L A )/. Given the DATA packet length L D, the maximal throughput i thu obtained a Φ = L ( D L + ( ) ( T S + T B ) / 1 e λρω ). + L C + L D + L A (11) It i hown that the maximal throughput Φ can be independent of the ource-to-detination ditance l. It alo increae monotonically with the node denity λ, approaching the limit decided by the radio data rate and the overhead ratio Expected end-to-end delay The end-to-end delay i given by I i=1 τ i. The expected endto-end delay Υ i ( I ) Υ= (a) E τ i i=1 = (b)[ E(I) 1 ] E ( τ 1 ) + τi = (c) l L + ( T S + T B ) + ( LC + L D + L A ) ( 1 e λρω ) E(Λ) + o ( l ), (12) where (a) i given by the definition; (b) i due to the fact that {τ i 1 i<i} are of identical probability ditribution [32]; and (c) i obtained directly from (5)and(7). Therefore, the expected end-to-end delay Υ increae linearly with the ource-to-detination ditance l. It alo decreae with the node denity λ, approaching a bounded limit Delay variance Let I c denote the number of the iteration with completed DATA tranmiion, that i, the number of wirele hop, and let I u denote the number of the iteration with uncompleted DATA tranmiion. I c and I u are independent random

9 L. Song and D. Hatzinako 9 variable, and I = I c + I u. The delay variance Θ i calculated by the following: ( I ) Θ= (a) σ 2 τ i i=1 ( ) = (b) σ 2 L + L (I C + L D + L 2 A c ) + T S + T B ( ) 2 + σ 2 L (I u ) + T S + T B [ (LC ) = (c) σ 2 + L (I D + L 2 A c ) +2 ( T S + T B + L ) ] ( ) + L D + L 2 A LC +σ 2 L (I) +T S+T B = (d) l σ { 2 (Λ) (1 E(Λ) 3 e λρω ) 2 [ (LC ) + L D + L 2 A +2 ( T S +T B + L ) ] ( ) + L D + L 2 } A L LC + + T S + T B [ (LC ) 1 e λρω +L l e D +L 2 A λρω +2 ( T S +T B + L ) E(Λ) ] + L D + L A LC + o(l), (13) where (a) i given by the definition; (b) i due to the analyi in Section 4.3.1, that i, the iterative delay under completed and uncompleted tranmiion repectively; (c) i due to the fact that σ 2 (I) = σ 2 (I c )+σ 2 (I u ); (d) i obtained from the reult of σ 2 (I) in(8), a well a the σ 2 (I c )derivedin(a.4) of Appendix A. Therefore, the end-to-end delay variance Θ increae linearly with the ource-to-detination ditance l. Θ alo diminihe to zero with the network denity λ, ince the component σ 2 (Λ) alo decreae to zero with higher λ Network energy conumption The expected network energy conumption for one DATA packet Ξ i ( I ) Ξ= (a) E ɛ i i=1 = (b)[ E(I) 1 ] E ( ɛ 1 ) + ɛi = (c) l E(ɛ 1) E(Λ) + o( l ), (14) where (a) and (b) are given by the definition and the fact that {ɛ i 1 i<i} are of identical probability ditribution [32]; (c) i obtained directly from (7), and the component ɛ 1 can be replaced by the reult in (6). Therefore, Ξ alo increae linearly with the ource-to-detination ditance l Optimization under QoS contraint Conider that the dataflow ha certain QoS requirement on throughput, expected end-to-end delay, and delay variance, for example, Φ Φ th, Υ Υ th,andθ Θ th.here,φ th, Υ th, and Θ th are the threhold contant repreenting thoe QoS contraint, repectively. The optimization aim to minimize the reource conumption, ubject to thee QoS contraint. Two type of reource conumption, including network energy conumption and network capacity, are conidered here, which lead to different optimization formulation. A pointed out in Section 4.2.4, thee two formulation can be repreenting different networking cenario. In capacity limited network, every node can have cabled power upply. The number of node i therefore limited by engineering cot, and the poible ue of long-range radio alo limit the available bandwidth. On the other hand, in energy limited network, the node can be powered by energy cavenging, for example, olar cell. Therefore, the number of wirele node can be large, which can alo ue hort-range radio with vat unlicened bandwidth. Thee contribute to large network capacity. Other work with imilar conideration have been dicued in [33] Capacity limited network The objective of the optimization i to minimize the tranmitting power P t, o a to limit the occupied geographic area of an unicat link. In accordance with the reult in [28], thi approach can ave the ue of wirele network capacity. Therefore, the optimization formulation i min P t ubject to: Φ Φ th ; Υ Υ th ; Θ Θ th Energy limited network (15) The objective of the optimization i to minimize the expected network energy conumption per DATA packet Ξ: min Ξ ubject to: Φ Φ th ; Υ Υ th ; Θ Θ th Solution and traffic priority (16) In order to olve the optimization in (15) and (16), the tranmitting power P t can be an ideal control knob (optimization variable), for the reource allocation. Both (15)and(16) can be found a convex optimization problem over P t, where efficient olution are guaranteed [34]. Specifically, a hown in (11), Φ monotonically increae with P t, ince Ω (defined in (4)) monotonically increae with P t, provided by any realitic channel model f (,, ). A hown in (12), Υ monotonically decreae with P t, ince E(Λ) monotonically increae with P t (proved in Appendix B).

10 10 International Journal of Digital Multimedia Broadcating Θ alo monotonically decreae with P t in (13), ince σ 2 (Λ) monotonically decreae with P t (proved in Appendix B). Moreover, a hown in (14), Ξ can be verified a a convex function of P t, where both E(ɛ 1 )ande(λ) monotonically increae with P t. Due to the decribed monotonicity, the convexity of the optimization in (15) and(16) can then be obtained, over the control knob P t. Therefore, we ugget that the tranmitting power P t can provide an effective control knob for the tradeoff between the reource conumption and the real-time QoS requirement. In fact, different level of P t can be configured for different clae of traffic, for example, voice, video (treaming), or data, which then decide the unicating priority. Further invetigation may alo include the radio data rate, oatogeneratetheproblemofjointpowerand rate control. Unfortunately, the convexity over cannot be obtained for a general channel propagation model f (,, ). Future tudie may addre the problem on ome pecific model Summary In ummary, the analytical reult indicate that all the invetigated real-time QoS metric can improve with larger network cale. In particular, the maximal throughput Φ increae with the network denity λ to a predetermined bound, while the expected end-to-end delay Υ decreae with λ, approaching a predetermined bound, a decided by other related parameter. On the other hand, the delay variance Θ fall to zero with higher network denity λ, which indicate that the network performance can be made arbitrarily table, imply by dropping more node in the network. For upporting long-range communication over a relatively large number of wirele hop, the analytical reult how that the maximal throughput Φ can be independent of the ource-to-detination ditance, while both the expected delay Υ and the delay variance Θ increae linearly with the ourceto-detination ditance. Furthermore, it i identified that the radio tranmitting power P t can be an ideal control knob for the tradeoff between the real-time QoS requirement and the reource conumption, for example, energy conumption or network capacity. We conider that all thee propoition could be intuitively conceivable for large-cale wirele network. Since the analytical reult are obtained under the long-range aymptotic condition l, they may preent the wortcae cenario analyi for practical conideration. In the next ection, the imulation reult are alo provided, howing how thee metric converge. 5. SIMULATION ESULTS Some fixed parameter in the imulation are lited in Table 2. In particular, the radio tranmiion parameter are conforming to the developed tet-bed, while other, for example, power conumption and channel model parameter, are typical for indoor hort-range radio. For example, Table 2: Parameter in the imulation. Parameter Value Unit ρ 0.2 N/A 250 K bit/ec L 120 bit L C, L A 96 bit L D 1024 bit T S 250 μec T B 750 μec P S, P 1 mw P I 100 μw β 3 N/A G 40 db N dbm/hz B 10 db α 4 N/A under thee parameter, the radio range i about meter, when the tranmitting power P t i 1 mw. Given the ource and the detination node, eparated by the ditance l, random Poion point are generated, for every iteration. The generation of the Poion point conform to the denity λ ρ on the 2D plane, which repreent the available wirele node DATA packet are then ent out conecutively from the ource to the detination, according to the decribed real-time unicat module. The end-to-end delay intance of every DATA packet i then recorded, from which the imulated end-to-end delay expectation and the imulated delay variance are etimated by tandard tatitical method. The imulated throughput i alo obtained, by dividing the data bulk ize 1000 L D with the time conumption of ending out the 1000 DATA packet at the ource. In the following figure, we alo compare the imulation reult to the analytical reult ide-by-ide, where the lower-order term, for example, o( l)oro(l), are neglected in plotting the analytical reult. Figure 4, 5, and6 how how the QoS metric change with the ource-to-detination ditance l, where the node denity λ i fixed at 0.1/m 2. Then Figure 7, 8, and9 how how the QoS metric change with the network denity λ, where the ource-to-detination l i fixed at 160 m. The number of wirele hop in the imulation can be up to 20. All the obtained imulation reult match well with the analytical reult, a well a the main propoition given in Section 4.6. Epecially, Figure 4, 5, and6 how that cloematching alo exit in hort-range unicating, although the analytical reult were obtained under long-range aymptotic approximation. Moreover, Figure 10 how how the expected network energy conumption per DATA packet change with the tranmitting power P t, which i oberved a a convex function. It i alo oberved in Figure 10 that the expected network energy conumption increae linearly with l, and it decreae with λ in the imulation. Thee imulation reult conform to previou performance analyi.

11 L. Song and D. Hatzinako 11 Throughput (bp) Source-to-detination ditance (m) P t = 0.2mWanalytical P t = 0.6mWanalytical P t = 1mWanalytical P t = 0.2 mw imulation P t = 0.6 mw imulation P t = 1 mw imulation Figure 4: Throughput veru ource-to-detination ditance l (λ = 0.1/m 2 ). End-to-end delay variance ( 2 ) Source-to-detination ditance (m) P t = 0.2mWanalytical P t = 0.6mWanalytical P t = 1mWanalytical P t = 0.2 mw imulation P t = 0.6 mw imulation P t = 1 mw imulation Figure 6: Delay variance veru ource-to-detination ditance l (λ = 0.1/m 2 ). Expected end-to-end delay () Source-to-detination ditance (m) P t = 0.2mWanalytical P t = 0.6mWanalytical P t = 1mWanalytical P t = 0.2 mw imulation P t = 0.6 mw imulation P t = 1 mw imulation Figure 5: Expected delay veru ource-to-detination ditance l (λ = 0.1/m 2 ). Throughput (bp) Network denity λ (1/m 2 ) P t = 0.2mWanalytical P t = 0.6mWanalytical P t = 1mWanalytical P t = 0.2 mw imulation P t = 0.6 mw imulation P t = 1 mw imulation Figure 7: Throughput veru network denity λ (l = 160 m). 6. EXPEIMENT ESULTS In the tet-bed (Figure 11), the ource and the detination node are connected to a laptop computer for the purpoe of collecting experiment record. The wirele node are baed on the cognitive radio prototype (Figure 1), with a tack of two radio board. The cognitive radio can opportunitically acce 3 data channel in 2.4 GHz band, a decribed in Section 3.2, where each channel ha an air-interface data rate 250 kbp a defined in IEEE [26]. The real-time unicat module i implemented in the cognitive radio prototype, with preconfigured addre. The packet length parameter, that i, L, L C, L D, L A,areabout the ame a thoe lited in Table 2. The node tranmitting power i however fixed at 15 dbm. The experiment reult on throughput, end-to-end delay expectation, and delay variance, are obtained by the following method. (1) Throughput i calculated at the ource, where the laptop computer forward 1000 DATA packet to the ource node.

12 12 International Journal of Digital Multimedia Broadcating 1.2 Expected end-to-end delay () Network denity λ (1/m 2 ) P t = 0.2mWanalytical P t = 0.6mWanalytical P t = 1mWanalytical P t = 0.2 mw imulation P t = 0.6 mw imulation P t = 1 mw imulation Figure 8: Expected delay veru network denity λ (l = 160 m). Expected network energy conumption (mj) Tranmitting power P t (mw) λ = 0.05 m 2, t = 100 m analytical λ = 0.1m 2, t = 100 m analytical λ = 0.1m 2, t = 200 m analytical λ = 0.05 m 2, t = 100 m imulation λ = 0.1m 2, t = 100 m imulation λ = 0.1m 2, t = 200 m imulation Figure 10: Network energy conumption veru tranmitting power P t. End-to-end delay variance ( 2 ) Network denity λ (1/m 2 ) P t = 0.2mWanalytical P t = 0.6mWanalytical P t = 1mWanalytical P t = 0.2 mw imulation P t = 0.6 mw imulation P t = 1 mw imulation Figure 9: Delay variance veru network denity λ (l = 160 m). Since the ource node accept a new DATA packet from the computer only after the previou one ha been ent out, the computer can count the total time expenditure in ending the 1000 DATA packet. The meaured throughput i obtained by dividing the bulk ize with the time expenditure. (2) On receiving a DATA packet, the detination alo forward the DATA packet to the laptop computer, where the end-toend delay intance of every DATA packet i calculated at the computer by the difference between the packet departure and arrival time. The meaured end-to-end delay expectation and Gateway Source elay Detination Figure 11: A naphot of the tet-bed. the delay variance are then obtained by tandard tatitical method. Since the operating ytem running at the laptop computer (Window XP Profeional Edition) i by no mean real-time, we tried to minimize the computer running thread in the experiment. The experiment were conducted in a tandard office environment, where external interference in the utilized 2.4 GHz band can be intenive, for example, from adjacent WLAN hot pot. Two et of experiment are illutrated in Figure 12. In Set-I, the node are placed in a traight line, uch that the four configuration correpond approximately to one-hop to four-hop communication. For example, in the econd configuration of Set-I, the node placement i uch that the chance of direct tranmiion from to d i mall. In Set-II, more relay node are added to every hop being illutrated in Figure 12. The purpoe of the twoet experiment i to compare how the deigned unicat module can improve the tet-bed QoS performance. In fact,